{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于lmList[1]中获取各指标对应的线性模型，对未来30期的数据进行预测，并与验证数据集进行比较分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.010060</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.005661</td>\n",
       "      <td>0.013739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.008562</td>\n",
       "      <td>0.009968</td>\n",
       "      <td>0.006515</td>\n",
       "      <td>0.013674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.001458</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.004146</td>\n",
       "      <td>0.001950</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.002785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.007166</td>\n",
       "      <td>0.007118</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.010414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.012999</td>\n",
       "      <td>0.006933</td>\n",
       "      <td>0.022305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.039191</td>\n",
       "      <td>0.045802</td>\n",
       "      <td>0.024576</td>\n",
       "      <td>0.052800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High        Low       Open      Close\n",
       "count  30.000000  30.000000  30.000000  30.000000\n",
       "mean    0.010060   0.009380   0.005661   0.013739\n",
       "std     0.008562   0.009968   0.006515   0.013674\n",
       "min     0.001458   0.000115   0.000114   0.000130\n",
       "25%     0.004146   0.001950   0.001653   0.002785\n",
       "50%     0.007166   0.007118   0.002913   0.010414\n",
       "75%     0.014652   0.012999   0.006933   0.022305\n",
       "max     0.039191   0.045802   0.024576   0.052800"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "with open('data/aicList.pkl', 'rb') as f:\n",
    "    aicList = pickle.load(f)\n",
    "    \n",
    "with open('data/data.pkl', 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "    \n",
    "with open('data/subdata_diff1.pkl', 'rb') as f:\n",
    "    subdata_diff1 = pickle.load(f)\n",
    "    \n",
    "with open('data/lmList.pkl', 'rb') as f:\n",
    "    lmList = pickle.load(f)\n",
    "    \n",
    "p = np.argmin(aicList)+1\n",
    "rows, cols = subdata_diff1.shape\n",
    "n = rows\n",
    "preddf = None\n",
    "for i in range(30):\n",
    "    predData = list(subdata_diff1[n+i-p:n+i].flatten())\n",
    "    predVals = np.matmul([1]+predData,lmList[p-1])\n",
    "    # 使用逆差分运算，还原预测值\n",
    "    predVals=data.iloc[n+i,:].values[:4]+predVals\n",
    "    if preddf is None:\n",
    "        preddf = [predVals]\n",
    "    else:\n",
    "        preddf = np.r_[preddf, [predVals]]\n",
    "    # 为subdata_diff1增加一条新记录\n",
    "    subdata_diff1 = np.r_[subdata_diff1, [data.iloc[n+i+1,:].values[:4] - data.iloc[n+i,:].values[:4]]]\n",
    "    \n",
    "#分析预测残差情况\n",
    "(np.abs(preddf - data.iloc[-30:data.shape[0],:4])/data.iloc[-30:data.shape[0],:4]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制二维图表观察预测数据与真实数据的逼近情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on test data: accuracy = 99.03% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "# 以下 font.family 设置仅适用于 Mac系统，其它系统请使用对应字体名称\n",
    "matplotlib.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "for i in range(4):\n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.plot(range(30),data.iloc[-30:data.shape[0],i].values,'o-',c='black')\n",
    "    plt.plot(range(30),preddf[:,i],'o--',c='gray')\n",
    "    plt.ylim(1000,1200)\n",
    "    plt.ylabel(\"$\"+data.columns[i]+\"$\")\n",
    "plt.show()\n",
    "v = 100*(1 - np.sum(np.abs(preddf - data.iloc[-30:data.shape[0],:4]).values)/np.sum(data.iloc[-30:data.shape[0],:4].values))\n",
    "print(\"Evaluation on test data: accuracy = %0.2f%% \\n\" % v)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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